code: def predict(self, df, args=None):
      args = self.model_storage.json_get("args")
      input_column = args["input_column"]
      if input_column not in df.columns:
          raise RuntimeError(f"input columns {input_column} not found ")
      input_list = df[input_column]
      if len(input_list) > 500:
          raise Exception(
              "Classifier only supports 500 data elements in list"
          )
      ml = MonkeyLearn(args["api_key"])
      df_list = []
      for text in input_list:
          pred_dict = {}
          classifier_response = ml.classifiers.classify(
              args["model_id"], [text]
          )
          for res_dict in classifier_response.body:
              if res_dict.get("error") is True:
                  raise Exception(res_dict["error_detail"])
              pred_dict["classification"] = res_dict["classifications"]
              pred_dict["tag"] = res_dict["classifications"][0][
                  "tag_name"
              ]
              df_list.append(pd.DataFrame([pred_dict]))
      pred_df = pd.concat(df_list)
      return pred_df
  
file_path: mindsdb/mindsdb/integrations/handlers/monkeylearn_handler/monkeylearn_handler.py
imports: from typing import Optional, Dict
  import pandas
  import requests
  from monkeylearn import MonkeyLearn
  from mindsdb.integrations.libs.base import BaseMLEngine
label_desc: Use image node_id 3 for calling the monkeylearnHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: pred_df